lale.lib.rasl.min_max_scaler module¶
- class lale.lib.rasl.min_max_scaler.MinMaxScaler(*, feature_range='(0, 1)', copy=True, clip=False)¶
Bases:
PlannedIndividualOp
Relational algebra implementation of MinMaxScaler.
This documentation is auto-generated from JSON schemas.
- Parameters
feature_range (tuple, >=2 items, <=2 items, not for optimizer, default (0, 1)) –
Desired range of transformed data.
item 0 : float, >=-1 for optimizer, <=0 for optimizer
item 1 : float, >=0.001 for optimizer, <=1 for optimizer
copy (True, not for optimizer, default True) – copy=True is the only value currently supported by this implementation
clip (boolean, optional, not for optimizer, default False) – Set to True to clip transformed values of held-out data to provided feature range.
Notes
constraint-1 : negated type of ‘X/isSparse’
MinMaxScaler does not support sparse input. Consider using MaxAbsScaler instead.
- fit(X, y=None, **fit_params)¶
Train the operator.
Note: The fit method is not available until this operator is trainable.
Once this method is available, it will have the following signature:
- Parameters
X (array of items : array of items : float) – Features; the outer array is over samples.
y (any type, optional) –
- partial_fit(X, y=None, **fit_params)¶
Incremental fit to train train the operator on a batch of samples.
Note: The partial_fit method is not available until this operator is trainable.
Once this method is available, it will have the following signature:
- transform(X, y=None)¶
Transform the data.
Note: The transform method is not available until this operator is trained.
Once this method is available, it will have the following signature:
- Parameters
X (array of items : array of items : float) – Features; the outer array is over samples.
- Returns
result – Output data schema for transformed data.
- Return type
array of items : array of items : float